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基于深度学习的室内可见光通信光源配置策略

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为了在不同的室内环境下寻求最优的光源优化方案,提出了一种基于深度学习的室内可见光通信光源配置策略.分别引入室内障碍物、自然光和人员移动干扰,以室内信噪比均匀度为目标函数,使用花授粉算法(FPA)对不同的房间状态下的光源功率和半功率角进行优化;将得到的房间状态和光源配置作为训练集,使用卷积神经网络(CNN)进行训练,得到的计算模型可以在不同房间状态下对最优光源设置进行预测,实现光源配置的动态调整.仿真结果表明:该策略的光源参数合格率达88%,且预测结果在房间信号功率和光照强度上均满足要求.
Light source configuration strategy for indoor visible light communication based on deep learning
In order to seek the optimal light source optimization scheme in different indoor environments,a deep learning based indoor visible light communication light source configuration strategy is proposed.Introducing indoor obstacles,natural light,and human movement interference separately,with indoor signal-to-noise ratio uniformity as the objective function,using the flower pollination algorithm(FPA)to optimize the light source power and half power angle under different room conditions.Using the obtained room state and light source configuration as the training set,a convolutional neural network(CNN)is used for training.The resulting computational model can predict the optimal light source settings in different room states,achieving dynamic ad-justment of light source configuration.The simulation results show that the qualification rate of the light source parameters of this strategy reaches 88%,and the predicted results meet the requirements in terms of room signal power and lighting intensity.

visible light communicationlight source layout schemeflower pollination algorithmconvolutional neural networksignal noise ratio uniformity

黄名川、王旭东、吴楠

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大连海事大学信息科学技术学院,辽宁大连 116026

可见光通信 光源布局策略 花授粉算法 卷积神经网络 信噪比均匀度

国家自然科学基金国家自然科学基金

6137109161801074

2024

光通信技术
中国电子科技集团公司第34研究所

光通信技术

北大核心
影响因子:0.372
ISSN:1002-5561
年,卷(期):2024.48(2)
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